Composite Likelihoods with Bounded Weights in Extrapolation of Data

TL;DR

This paper introduces a statistical method using bounded composite likelihoods to improve extrapolation of data from adult to pediatric populations, ensuring more reliable estimates by accounting for variability and similarity bounds.

Contribution

It proposes a novel composite likelihood approach with bounded weights for data extrapolation, linking frequentist and Bayesian methods for better inference.

Findings

Effective in borrowing information across populations.

Provides asymptotic distributions for parameter estimates.

Ensures robustness by bounding similarity weights.

Abstract

Among many efforts to facilitate timely access to safe and effective medicines to children, increased attention has been given to extrapolation. Loosely, it is the leveraging of conclusions or available data from adults or older age groups to draw conclusions for the target pediatric population when it can be assumed that the course of the disease and the expected response to a medicinal product would be sufficiently similar in the pediatric and the reference population. Extrapolation then can be characterized as a statistical mapping of information from the reference (adults or older age groups) to the target pediatric population. The translation, or loosely mapping of information, can be through a composite likelihood approach where the likelihood of the reference population is weighted by exponentiation and that this exponent is related to the value of the mapped information in the target population. The weight is bounded above and below recognizing the fact that similarity (of the disease and the expected response) is still valid despite variability of response between the cohorts. Maximum likelihood approaches are then used for estimation of parameters and asymptotic theory is used to derive distributions of estimates for use in inference. Hence, the estimation of effects in the target population borrows information from reference population. In addition, this manuscript also talks about how this method is related to the Bayesian statistical paradigm.